Overview

Dataset statistics

Number of variables23
Number of observations236
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.2 KiB
Average record size in memory192.0 B

Variable types

Numeric16
Categorical7

Alerts

Card_Category has constant value ""Constant
Customer_Age is highly overall correlated with Months_on_bookHigh correlation
Months_on_book is highly overall correlated with Customer_AgeHigh correlation
Months_Inactive_12_mon is highly overall correlated with Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 and 1 other fieldsHigh correlation
Contacts_Count_12_mon is highly overall correlated with Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 and 1 other fieldsHigh correlation
Credit_Limit is highly overall correlated with Avg_Open_To_Buy and 1 other fieldsHigh correlation
Total_Revolving_Bal is highly overall correlated with Avg_Utilization_Ratio and 1 other fieldsHigh correlation
Avg_Open_To_Buy is highly overall correlated with Credit_Limit and 1 other fieldsHigh correlation
Total_Trans_Amt is highly overall correlated with Total_Trans_CtHigh correlation
Total_Trans_Ct is highly overall correlated with Total_Trans_Amt and 1 other fieldsHigh correlation
Avg_Utilization_Ratio is highly overall correlated with Credit_Limit and 2 other fieldsHigh correlation
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 is highly overall correlated with Months_Inactive_12_mon and 3 other fieldsHigh correlation
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2 is highly overall correlated with Months_Inactive_12_mon and 3 other fieldsHigh correlation
Attrition_Flag is highly overall correlated with Total_Revolving_Bal and 3 other fieldsHigh correlation
Gender is highly overall correlated with Income_CategoryHigh correlation
Income_Category is highly overall correlated with GenderHigh correlation
CLIENTNUM has unique valuesUnique
Dependent_count has 13 (5.5%) zerosZeros
Contacts_Count_12_mon has 12 (5.1%) zerosZeros

Reproduction

Analysis started2023-11-06 07:52:26.739139
Analysis finished2023-11-06 07:52:45.989737
Duration19.25 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

CLIENTNUM
Real number (ℝ)

UNIQUE 

Distinct236
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3771506 × 108
Minimum7.0808696 × 108
Maximum8.2828833 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:46.073967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7.0808696 × 108
5-th percentile7.0892109 × 108
Q17.1282461 × 108
median7.1682127 × 108
Q37.7246521 × 108
95-th percentile8.184641 × 108
Maximum8.2828833 × 108
Range1.2020138 × 108
Interquartile range (IQR)59640600

Descriptive statistics

Standard deviation37241902
Coefficient of variation (CV)0.050482774
Kurtosis-0.25852112
Mean7.3771506 × 108
Median Absolute Deviation (MAD)4938712.5
Skewness1.1470084
Sum1.7410075 × 1011
Variance1.3869593 × 1015
MonotonicityNot monotonic
2023-11-06T10:52:46.172387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
784725333 1
 
0.4%
713772333 1
 
0.4%
772243833 1
 
0.4%
789095958 1
 
0.4%
708397008 1
 
0.4%
822121608 1
 
0.4%
779405133 1
 
0.4%
789907533 1
 
0.4%
823908858 1
 
0.4%
716689383 1
 
0.4%
Other values (226) 226
95.8%
ValueCountFrequency (%)
708086958 1
0.4%
708134283 1
0.4%
708334158 1
0.4%
708397008 1
0.4%
708426483 1
0.4%
708575058 1
0.4%
708597558 1
0.4%
708702258 1
0.4%
708829758 1
0.4%
708860358 1
0.4%
ValueCountFrequency (%)
828288333 1
0.4%
827964858 1
0.4%
827901183 1
0.4%
826548708 1
0.4%
826467333 1
0.4%
823928058 1
0.4%
823908858 1
0.4%
823621083 1
0.4%
822121608 1
0.4%
821759658 1
0.4%

Attrition_Flag
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Existing Customer
186 
Attrited Customer
50 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters4012
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExisting Customer
2nd rowExisting Customer
3rd rowExisting Customer
4th rowExisting Customer
5th rowExisting Customer

Common Values

ValueCountFrequency (%)
Existing Customer 186
78.8%
Attrited Customer 50
 
21.2%

Length

2023-11-06T10:52:46.262210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T10:52:46.340398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
customer 236
50.0%
existing 186
39.4%
attrited 50
 
10.6%

Most occurring characters

ValueCountFrequency (%)
t 572
14.3%
i 422
10.5%
s 422
10.5%
e 286
 
7.1%
r 286
 
7.1%
236
 
5.9%
C 236
 
5.9%
u 236
 
5.9%
o 236
 
5.9%
m 236
 
5.9%
Other values (6) 844
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3304
82.4%
Uppercase Letter 472
 
11.8%
Space Separator 236
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 572
17.3%
i 422
12.8%
s 422
12.8%
e 286
8.7%
r 286
8.7%
u 236
7.1%
o 236
7.1%
m 236
7.1%
x 186
 
5.6%
n 186
 
5.6%
Other values (2) 236
7.1%
Uppercase Letter
ValueCountFrequency (%)
C 236
50.0%
E 186
39.4%
A 50
 
10.6%
Space Separator
ValueCountFrequency (%)
236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3776
94.1%
Common 236
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 572
15.1%
i 422
11.2%
s 422
11.2%
e 286
7.6%
r 286
7.6%
C 236
 
6.2%
u 236
 
6.2%
o 236
 
6.2%
m 236
 
6.2%
E 186
 
4.9%
Other values (5) 658
17.4%
Common
ValueCountFrequency (%)
236
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 572
14.3%
i 422
10.5%
s 422
10.5%
e 286
 
7.1%
r 286
 
7.1%
236
 
5.9%
C 236
 
5.9%
u 236
 
5.9%
o 236
 
5.9%
m 236
 
5.9%
Other values (6) 844
21.0%

Customer_Age
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.915254
Minimum29
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:46.403504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile35
Q142
median47.5
Q351
95-th percentile57.25
Maximum60
Range31
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.803724
Coefficient of variation (CV)0.14502157
Kurtosis-0.51037152
Mean46.915254
Median Absolute Deviation (MAD)4.5
Skewness-0.24171156
Sum11072
Variance46.29066
MonotonicityNot monotonic
2023-11-06T10:52:46.497855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
51 18
 
7.6%
45 17
 
7.2%
48 16
 
6.8%
49 14
 
5.9%
54 12
 
5.1%
50 12
 
5.1%
42 12
 
5.1%
47 11
 
4.7%
46 11
 
4.7%
56 9
 
3.8%
Other values (20) 104
44.1%
ValueCountFrequency (%)
29 1
 
0.4%
30 1
 
0.4%
31 2
 
0.8%
34 5
2.1%
35 6
2.5%
36 2
 
0.8%
37 8
3.4%
38 7
3.0%
39 7
3.0%
40 5
2.1%
ValueCountFrequency (%)
60 2
 
0.8%
59 8
3.4%
58 2
 
0.8%
57 6
 
2.5%
56 9
3.8%
55 7
 
3.0%
54 12
5.1%
53 5
 
2.1%
52 7
 
3.0%
51 18
7.6%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
F
127 
M
109 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters236
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
F 127
53.8%
M 109
46.2%

Length

2023-11-06T10:52:46.591629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T10:52:46.654122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
f 127
53.8%
m 109
46.2%

Most occurring characters

ValueCountFrequency (%)
F 127
53.8%
M 109
46.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 236
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 127
53.8%
M 109
46.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 236
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 127
53.8%
M 109
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 236
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 127
53.8%
M 109
46.2%

Dependent_count
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4872881
Minimum0
Maximum5
Zeros13
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:46.701605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1867385
Coefficient of variation (CV)0.47712146
Kurtosis-0.24529301
Mean2.4872881
Median Absolute Deviation (MAD)1
Skewness-0.061956383
Sum587
Variance1.4083484
MonotonicityNot monotonic
2023-11-06T10:52:46.780210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 79
33.5%
2 69
29.2%
1 33
14.0%
4 31
 
13.1%
0 13
 
5.5%
5 11
 
4.7%
ValueCountFrequency (%)
0 13
 
5.5%
1 33
14.0%
2 69
29.2%
3 79
33.5%
4 31
 
13.1%
5 11
 
4.7%
ValueCountFrequency (%)
5 11
 
4.7%
4 31
 
13.1%
3 79
33.5%
2 69
29.2%
1 33
14.0%
0 13
 
5.5%

Education_Level
Categorical

Distinct6
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Graduate
86 
High School
47 
Uneducated
39 
Unknown
27 
College
21 

Length

Max length11
Median length10
Mean length8.7923729
Min length7

Characters and Unicode

Total characters2075
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh School
2nd rowGraduate
3rd rowGraduate
4th rowDoctorate
5th rowUneducated

Common Values

ValueCountFrequency (%)
Graduate 86
36.4%
High School 47
19.9%
Uneducated 39
16.5%
Unknown 27
 
11.4%
College 21
 
8.9%
Doctorate 16
 
6.8%

Length

2023-11-06T10:52:46.874569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T10:52:46.961303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
graduate 86
30.4%
high 47
16.6%
school 47
16.6%
uneducated 39
13.8%
unknown 27
 
9.5%
college 21
 
7.4%
doctorate 16
 
5.7%

Most occurring characters

ValueCountFrequency (%)
a 227
 
10.9%
e 222
 
10.7%
o 174
 
8.4%
d 164
 
7.9%
t 157
 
7.6%
u 125
 
6.0%
n 120
 
5.8%
r 102
 
4.9%
c 102
 
4.9%
h 94
 
4.5%
Other values (12) 588
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1745
84.1%
Uppercase Letter 283
 
13.6%
Space Separator 47
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 227
13.0%
e 222
12.7%
o 174
10.0%
d 164
9.4%
t 157
9.0%
u 125
7.2%
n 120
6.9%
r 102
 
5.8%
c 102
 
5.8%
h 94
 
5.4%
Other values (5) 258
14.8%
Uppercase Letter
ValueCountFrequency (%)
G 86
30.4%
U 66
23.3%
S 47
16.6%
H 47
16.6%
C 21
 
7.4%
D 16
 
5.7%
Space Separator
ValueCountFrequency (%)
47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2028
97.7%
Common 47
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 227
11.2%
e 222
10.9%
o 174
 
8.6%
d 164
 
8.1%
t 157
 
7.7%
u 125
 
6.2%
n 120
 
5.9%
r 102
 
5.0%
c 102
 
5.0%
h 94
 
4.6%
Other values (11) 541
26.7%
Common
ValueCountFrequency (%)
47
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2075
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 227
 
10.9%
e 222
 
10.7%
o 174
 
8.4%
d 164
 
7.9%
t 157
 
7.6%
u 125
 
6.0%
n 120
 
5.8%
r 102
 
4.9%
c 102
 
4.9%
h 94
 
4.5%
Other values (12) 588
28.3%

Marital_Status
Categorical

Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Married
109 
Single
94 
Unknown
19 
Divorced
14 

Length

Max length8
Median length7
Mean length6.6610169
Min length6

Characters and Unicode

Total characters1572
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowUnknown
3rd rowMarried
4th rowSingle
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 109
46.2%
Single 94
39.8%
Unknown 19
 
8.1%
Divorced 14
 
5.9%

Length

2023-11-06T10:52:47.064363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T10:52:47.142335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
married 109
46.2%
single 94
39.8%
unknown 19
 
8.1%
divorced 14
 
5.9%

Most occurring characters

ValueCountFrequency (%)
r 232
14.8%
i 217
13.8%
e 217
13.8%
n 151
9.6%
d 123
7.8%
M 109
6.9%
a 109
6.9%
l 94
6.0%
g 94
6.0%
S 94
6.0%
Other values (7) 132
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1336
85.0%
Uppercase Letter 236
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 232
17.4%
i 217
16.2%
e 217
16.2%
n 151
11.3%
d 123
9.2%
a 109
8.2%
l 94
7.0%
g 94
7.0%
o 33
 
2.5%
k 19
 
1.4%
Other values (3) 47
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
M 109
46.2%
S 94
39.8%
U 19
 
8.1%
D 14
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 1572
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 232
14.8%
i 217
13.8%
e 217
13.8%
n 151
9.6%
d 123
7.8%
M 109
6.9%
a 109
6.9%
l 94
6.0%
g 94
6.0%
S 94
6.0%
Other values (7) 132
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 232
14.8%
i 217
13.8%
e 217
13.8%
n 151
9.6%
d 123
7.8%
M 109
6.9%
a 109
6.9%
l 94
6.0%
g 94
6.0%
S 94
6.0%
Other values (7) 132
8.4%

Income_Category
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Less than $40K
76 
$40K - $60K
47 
$60K - $80K
39 
Unknown
34 
$80K - $120K
26 

Length

Max length14
Median length12
Mean length11.262712
Min length7

Characters and Unicode

Total characters2658
Distinct characters22
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$40K - $60K
2nd row$40K - $60K
3rd rowUnknown
4th rowLess than $40K
5th row$40K - $60K

Common Values

ValueCountFrequency (%)
Less than $40K 76
32.2%
$40K - $60K 47
19.9%
$60K - $80K 39
16.5%
Unknown 34
14.4%
$80K - $120K 26
 
11.0%
$120K + 14
 
5.9%

Length

2023-11-06T10:52:47.235935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T10:52:47.384707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
126
20.1%
40k 123
19.6%
60k 86
13.7%
less 76
12.1%
than 76
12.1%
80k 65
10.4%
120k 40
 
6.4%
unknown 34
 
5.4%

Most occurring characters

ValueCountFrequency (%)
390
14.7%
K 314
11.8%
0 314
11.8%
$ 314
11.8%
n 178
 
6.7%
s 152
 
5.7%
4 123
 
4.6%
- 112
 
4.2%
6 86
 
3.2%
e 76
 
2.9%
Other values (12) 599
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 736
27.7%
Decimal Number 668
25.1%
Uppercase Letter 424
16.0%
Space Separator 390
14.7%
Currency Symbol 314
11.8%
Dash Punctuation 112
 
4.2%
Math Symbol 14
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 178
24.2%
s 152
20.7%
e 76
10.3%
a 76
10.3%
h 76
10.3%
t 76
10.3%
k 34
 
4.6%
o 34
 
4.6%
w 34
 
4.6%
Decimal Number
ValueCountFrequency (%)
0 314
47.0%
4 123
 
18.4%
6 86
 
12.9%
8 65
 
9.7%
1 40
 
6.0%
2 40
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
K 314
74.1%
L 76
 
17.9%
U 34
 
8.0%
Space Separator
ValueCountFrequency (%)
390
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 314
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 112
100.0%
Math Symbol
ValueCountFrequency (%)
+ 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1498
56.4%
Latin 1160
43.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 314
27.1%
n 178
15.3%
s 152
13.1%
e 76
 
6.6%
L 76
 
6.6%
a 76
 
6.6%
h 76
 
6.6%
t 76
 
6.6%
U 34
 
2.9%
k 34
 
2.9%
Other values (2) 68
 
5.9%
Common
ValueCountFrequency (%)
390
26.0%
0 314
21.0%
$ 314
21.0%
4 123
 
8.2%
- 112
 
7.5%
6 86
 
5.7%
8 65
 
4.3%
1 40
 
2.7%
2 40
 
2.7%
+ 14
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
390
14.7%
K 314
11.8%
0 314
11.8%
$ 314
11.8%
n 178
 
6.7%
s 152
 
5.7%
4 123
 
4.6%
- 112
 
4.2%
6 86
 
3.2%
e 76
 
2.9%
Other values (12) 599
22.5%

Card_Category
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Blue
236 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters944
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlue
3rd rowBlue
4th rowBlue
5th rowBlue

Common Values

ValueCountFrequency (%)
Blue 236
100.0%

Length

2023-11-06T10:52:47.589547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T10:52:47.651933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
blue 236
100.0%

Most occurring characters

ValueCountFrequency (%)
B 236
25.0%
l 236
25.0%
u 236
25.0%
e 236
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 708
75.0%
Uppercase Letter 236
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 236
33.3%
u 236
33.3%
e 236
33.3%
Uppercase Letter
ValueCountFrequency (%)
B 236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 944
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 236
25.0%
l 236
25.0%
u 236
25.0%
e 236
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 236
25.0%
l 236
25.0%
u 236
25.0%
e 236
25.0%

Months_on_book
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.254237
Minimum13
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:47.714827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile23
Q132
median36
Q341
95-th percentile48
Maximum54
Range41
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.2595486
Coefficient of variation (CV)0.20024
Kurtosis0.19787446
Mean36.254237
Median Absolute Deviation (MAD)4
Skewness-0.20608805
Sum8556
Variance52.701046
MonotonicityNot monotonic
2023-11-06T10:52:47.811527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
36 55
23.3%
40 11
 
4.7%
39 11
 
4.7%
41 11
 
4.7%
35 10
 
4.2%
46 10
 
4.2%
37 9
 
3.8%
43 9
 
3.8%
28 8
 
3.4%
32 7
 
3.0%
Other values (26) 95
40.3%
ValueCountFrequency (%)
13 1
 
0.4%
19 3
 
1.3%
20 2
 
0.8%
22 5
2.1%
23 3
 
1.3%
24 1
 
0.4%
25 3
 
1.3%
26 6
2.5%
27 5
2.1%
28 8
3.4%
ValueCountFrequency (%)
54 1
 
0.4%
53 2
 
0.8%
52 1
 
0.4%
51 1
 
0.4%
50 2
 
0.8%
49 4
 
1.7%
48 2
 
0.8%
47 7
3.0%
46 10
4.2%
45 3
 
1.3%
Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
3
100 
4
77 
2
59 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters236
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row2
5th row4

Common Values

ValueCountFrequency (%)
3 100
42.4%
4 77
32.6%
2 59
25.0%

Length

2023-11-06T10:52:47.878089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T10:52:47.940979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 100
42.4%
4 77
32.6%
2 59
25.0%

Most occurring characters

ValueCountFrequency (%)
3 100
42.4%
4 77
32.6%
2 59
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 236
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 100
42.4%
4 77
32.6%
2 59
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 236
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 100
42.4%
4 77
32.6%
2 59
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 236
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 100
42.4%
4 77
32.6%
2 59
25.0%

Months_Inactive_12_mon
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4491525
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:48.019582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0766177
Coefficient of variation (CV)0.43958785
Kurtosis1.6293992
Mean2.4491525
Median Absolute Deviation (MAD)1
Skewness0.8652136
Sum578
Variance1.1591057
MonotonicityNot monotonic
2023-11-06T10:52:48.098268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 96
40.7%
2 73
30.9%
1 47
19.9%
4 9
 
3.8%
6 6
 
2.5%
5 5
 
2.1%
ValueCountFrequency (%)
1 47
19.9%
2 73
30.9%
3 96
40.7%
4 9
 
3.8%
5 5
 
2.1%
6 6
 
2.5%
ValueCountFrequency (%)
6 6
 
2.5%
5 5
 
2.1%
4 9
 
3.8%
3 96
40.7%
2 73
30.9%
1 47
19.9%

Contacts_Count_12_mon
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4915254
Minimum0
Maximum5
Zeros12
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:48.162905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q12
median3
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0895696
Coefficient of variation (CV)0.43731025
Kurtosis-0.1872477
Mean2.4915254
Median Absolute Deviation (MAD)1
Skewness-0.39618076
Sum588
Variance1.1871619
MonotonicityNot monotonic
2023-11-06T10:52:48.239515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 94
39.8%
2 62
26.3%
4 34
 
14.4%
1 31
 
13.1%
0 12
 
5.1%
5 3
 
1.3%
ValueCountFrequency (%)
0 12
 
5.1%
1 31
 
13.1%
2 62
26.3%
3 94
39.8%
4 34
 
14.4%
5 3
 
1.3%
ValueCountFrequency (%)
5 3
 
1.3%
4 34
 
14.4%
3 94
39.8%
2 62
26.3%
1 31
 
13.1%
0 12
 
5.1%

Credit_Limit
Real number (ℝ)

HIGH CORRELATION 

Distinct233
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5730.9831
Minimum3022
Maximum9959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:48.330397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3022
5-th percentile3137.75
Q13854.5
median5250.5
Q37470
95-th percentile9365.5
Maximum9959
Range6937
Interquartile range (IQR)3615.5

Descriptive statistics

Standard deviation2087.4199
Coefficient of variation (CV)0.36423417
Kurtosis-1.1254706
Mean5730.9831
Median Absolute Deviation (MAD)1656
Skewness0.45669875
Sum1352512
Variance4357321.7
MonotonicityNot monotonic
2023-11-06T10:52:48.433814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3137 2
 
0.8%
9959 2
 
0.8%
4196 2
 
0.8%
4170 1
 
0.4%
7106 1
 
0.4%
8446 1
 
0.4%
3186 1
 
0.4%
7603 1
 
0.4%
3805 1
 
0.4%
7116 1
 
0.4%
Other values (223) 223
94.5%
ValueCountFrequency (%)
3022 1
0.4%
3034 1
0.4%
3054 1
0.4%
3073 1
0.4%
3085 1
0.4%
3097 1
0.4%
3104 1
0.4%
3108 1
0.4%
3128 1
0.4%
3131 1
0.4%
ValueCountFrequency (%)
9959 2
0.8%
9863 1
0.4%
9749 1
0.4%
9705 1
0.4%
9703 1
0.4%
9595 1
0.4%
9589 1
0.4%
9546 1
0.4%
9463 1
0.4%
9457 1
0.4%

Total_Revolving_Bal
Real number (ℝ)

HIGH CORRELATION 

Distinct185
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean752.74576
Minimum134
Maximum996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:48.544291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum134
5-th percentile342
Q1659
median778.5
Q3906.5
95-th percentile982.75
Maximum996
Range862
Interquartile range (IQR)247.5

Descriptive statistics

Standard deviation192.4861
Coefficient of variation (CV)0.25571196
Kurtosis1.156547
Mean752.74576
Median Absolute Deviation (MAD)125
Skewness-1.1036054
Sum177648
Variance37050.897
MonotonicityNot monotonic
2023-11-06T10:52:48.638617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
795 4
 
1.7%
622 3
 
1.3%
764 3
 
1.3%
859 3
 
1.3%
938 3
 
1.3%
710 3
 
1.3%
845 3
 
1.3%
996 2
 
0.8%
659 2
 
0.8%
847 2
 
0.8%
Other values (175) 208
88.1%
ValueCountFrequency (%)
134 1
0.4%
145 1
0.4%
157 1
0.4%
193 1
0.4%
199 1
0.4%
204 1
0.4%
211 1
0.4%
232 1
0.4%
243 1
0.4%
274 1
0.4%
ValueCountFrequency (%)
996 2
0.8%
995 2
0.8%
994 1
0.4%
993 1
0.4%
990 1
0.4%
989 1
0.4%
986 2
0.8%
985 2
0.8%
982 1
0.4%
981 1
0.4%

Avg_Open_To_Buy
Real number (ℝ)

HIGH CORRELATION 

Distinct233
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4978.2373
Minimum2119
Maximum9760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:48.735994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2119
5-th percentile2316.75
Q13119.75
median4444.5
Q36766
95-th percentile8636.25
Maximum9760
Range7641
Interquartile range (IQR)3646.25

Descriptive statistics

Standard deviation2102.8769
Coefficient of variation (CV)0.42241396
Kurtosis-1.0717561
Mean4978.2373
Median Absolute Deviation (MAD)1656.5
Skewness0.45741872
Sum1174864
Variance4422091.4
MonotonicityNot monotonic
2023-11-06T10:52:48.852488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3495 2
 
0.8%
2776 2
 
0.8%
2732 2
 
0.8%
2980 1
 
0.4%
2218 1
 
0.4%
6859 1
 
0.4%
2984 1
 
0.4%
6423 1
 
0.4%
5093 1
 
0.4%
4383 1
 
0.4%
Other values (223) 223
94.5%
ValueCountFrequency (%)
2119 1
0.4%
2127 1
0.4%
2152 1
0.4%
2178 1
0.4%
2192 1
0.4%
2201 1
0.4%
2209 1
0.4%
2218 1
0.4%
2242 1
0.4%
2263 1
0.4%
ValueCountFrequency (%)
9760 1
0.4%
9225 1
0.4%
9177 1
0.4%
9107 1
0.4%
9086 1
0.4%
8961 1
0.4%
8959 1
0.4%
8890 1
0.4%
8855 1
0.4%
8762 1
0.4%

Total_Amt_Chng_Q4_Q1
Real number (ℝ)

Distinct197
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74569068
Minimum0.304
Maximum2.275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:48.958249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.304
5-th percentile0.4785
Q10.63
median0.7215
Q30.84775
95-th percentile1.02525
Maximum2.275
Range1.971
Interquartile range (IQR)0.21775

Descriptive statistics

Standard deviation0.21151698
Coefficient of variation (CV)0.28365244
Kurtosis13.168707
Mean0.74569068
Median Absolute Deviation (MAD)0.112
Skewness2.2960527
Sum175.983
Variance0.044739432
MonotonicityNot monotonic
2023-11-06T10:52:49.068774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.504 3
 
1.3%
0.681 3
 
1.3%
0.765 3
 
1.3%
0.731 3
 
1.3%
0.647 3
 
1.3%
0.673 3
 
1.3%
0.604 3
 
1.3%
0.958 2
 
0.8%
0.485 2
 
0.8%
0.815 2
 
0.8%
Other values (187) 209
88.6%
ValueCountFrequency (%)
0.304 1
0.4%
0.307 1
0.4%
0.35 1
0.4%
0.396 1
0.4%
0.417 1
0.4%
0.422 1
0.4%
0.432 1
0.4%
0.433 1
0.4%
0.446 1
0.4%
0.456 1
0.4%
ValueCountFrequency (%)
2.275 1
0.4%
1.705 1
0.4%
1.608 1
0.4%
1.32 1
0.4%
1.193 1
0.4%
1.084 1
0.4%
1.079 1
0.4%
1.072 1
0.4%
1.057 1
0.4%
1.052 1
0.4%

Total_Trans_Amt
Real number (ℝ)

HIGH CORRELATION 

Distinct227
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4756.6525
Minimum791
Maximum16737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:49.164113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum791
5-th percentile1195.25
Q12329.25
median3967.5
Q34837.5
95-th percentile14800.5
Maximum16737
Range15946
Interquartile range (IQR)2508.25

Descriptive statistics

Standard deviation3914.1169
Coefficient of variation (CV)0.82287216
Kurtosis2.4703346
Mean4756.6525
Median Absolute Deviation (MAD)1234
Skewness1.8491939
Sum1122570
Variance15320311
MonotonicityNot monotonic
2023-11-06T10:52:49.287305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4077 2
 
0.8%
2051 2
 
0.8%
2781 2
 
0.8%
3877 2
 
0.8%
1193 2
 
0.8%
14596 2
 
0.8%
4828 2
 
0.8%
4121 2
 
0.8%
4399 2
 
0.8%
3100 1
 
0.4%
Other values (217) 217
91.9%
ValueCountFrequency (%)
791 1
0.4%
842 1
0.4%
870 1
0.4%
911 1
0.4%
931 1
0.4%
990 1
0.4%
999 1
0.4%
1002 1
0.4%
1138 1
0.4%
1165 1
0.4%
ValueCountFrequency (%)
16737 1
0.4%
16732 1
0.4%
16179 1
0.4%
15903 1
0.4%
15865 1
0.4%
15471 1
0.4%
15443 1
0.4%
15380 1
0.4%
15352 1
0.4%
15102 1
0.4%

Total_Trans_Ct
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.177966
Minimum18
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:49.390114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile27.75
Q147.75
median70
Q382
95-th percentile109
Maximum124
Range106
Interquartile range (IQR)34.25

Descriptive statistics

Standard deviation24.635256
Coefficient of variation (CV)0.36671632
Kurtosis-0.60437872
Mean67.177966
Median Absolute Deviation (MAD)17
Skewness0.041867287
Sum15854
Variance606.89585
MonotonicityNot monotonic
2023-11-06T10:52:49.499933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 9
 
3.8%
79 8
 
3.4%
77 8
 
3.4%
38 8
 
3.4%
88 7
 
3.0%
59 6
 
2.5%
75 6
 
2.5%
74 5
 
2.1%
70 5
 
2.1%
35 5
 
2.1%
Other values (80) 169
71.6%
ValueCountFrequency (%)
18 1
 
0.4%
21 1
 
0.4%
22 3
1.3%
24 3
1.3%
25 1
 
0.4%
26 2
0.8%
27 1
 
0.4%
28 4
1.7%
30 2
0.8%
31 4
1.7%
ValueCountFrequency (%)
124 1
 
0.4%
123 1
 
0.4%
122 2
0.8%
120 2
0.8%
117 1
 
0.4%
116 1
 
0.4%
115 1
 
0.4%
111 1
 
0.4%
110 1
 
0.4%
109 3
1.3%

Total_Ct_Chng_Q4_Q1
Real number (ℝ)

Distinct178
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68417797
Minimum0.097
Maximum1.571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:49.578938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.097
5-th percentile0.34275
Q10.57325
median0.6835
Q30.78475
95-th percentile1
Maximum1.571
Range1.474
Interquartile range (IQR)0.2115

Descriptive statistics

Standard deviation0.21044331
Coefficient of variation (CV)0.30758562
Kurtosis2.4503874
Mean0.68417797
Median Absolute Deviation (MAD)0.108
Skewness0.65902102
Sum161.466
Variance0.044286385
MonotonicityNot monotonic
2023-11-06T10:52:49.688993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 6
 
2.5%
0.581 4
 
1.7%
0.556 4
 
1.7%
0.75 4
 
1.7%
0.727 3
 
1.3%
0.723 3
 
1.3%
0.733 3
 
1.3%
0.64 3
 
1.3%
0.722 3
 
1.3%
0.667 3
 
1.3%
Other values (168) 200
84.7%
ValueCountFrequency (%)
0.097 1
0.4%
0.2 1
0.4%
0.207 1
0.4%
0.231 1
0.4%
0.263 1
0.4%
0.273 1
0.4%
0.29 1
0.4%
0.3 1
0.4%
0.308 1
0.4%
0.31 1
0.4%
ValueCountFrequency (%)
1.571 1
0.4%
1.5 1
0.4%
1.417 1
0.4%
1.273 1
0.4%
1.258 1
0.4%
1.2 1
0.4%
1.147 1
0.4%
1.083 2
0.8%
1.034 1
0.4%
1.025 1
0.4%

Avg_Utilization_Ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct149
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15019915
Minimum0.016
Maximum0.314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:49.783066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.016
5-th percentile0.053
Q10.09975
median0.144
Q30.189
95-th percentile0.27625
Maximum0.314
Range0.298
Interquartile range (IQR)0.08925

Descriptive statistics

Standard deviation0.066963936
Coefficient of variation (CV)0.44583431
Kurtosis-0.32406737
Mean0.15019915
Median Absolute Deviation (MAD)0.045
Skewness0.4569404
Sum35.447
Variance0.0044841687
MonotonicityNot monotonic
2023-11-06T10:52:49.892908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.126 5
 
2.1%
0.109 5
 
2.1%
0.097 4
 
1.7%
0.216 4
 
1.7%
0.155 4
 
1.7%
0.102 4
 
1.7%
0.189 4
 
1.7%
0.158 4
 
1.7%
0.053 3
 
1.3%
0.122 3
 
1.3%
Other values (139) 196
83.1%
ValueCountFrequency (%)
0.016 1
 
0.4%
0.017 1
 
0.4%
0.02 2
0.8%
0.037 1
 
0.4%
0.039 1
 
0.4%
0.04 1
 
0.4%
0.042 1
 
0.4%
0.044 1
 
0.4%
0.05 1
 
0.4%
0.053 3
1.3%
ValueCountFrequency (%)
0.314 1
0.4%
0.313 1
0.4%
0.309 1
0.4%
0.306 2
0.8%
0.304 1
0.4%
0.3 1
0.4%
0.296 1
0.4%
0.294 1
0.4%
0.279 1
0.4%
0.278 1
0.4%
Distinct175
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21109205
Minimum2.0889 × 10-5
Maximum0.99893
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:50.003424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.0889 × 10-5
5-th percentile4.633925 × 10-5
Q10.000100661
median0.000195395
Q30.00052052
95-th percentile0.9971875
Maximum0.99893
Range0.99890911
Interquartile range (IQR)0.000419859

Descriptive statistics

Standard deviation0.40762891
Coefficient of variation (CV)1.9310481
Kurtosis0.01456094
Mean0.21109205
Median Absolute Deviation (MAD)0.000122262
Skewness1.4193182
Sum49.817725
Variance0.16616133
MonotonicityNot monotonic
2023-11-06T10:52:50.113263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00018665 6
 
2.5%
0.00019864 5
 
2.1%
0.99499 5
 
2.1%
3.3214 × 10-54
 
1.7%
0.00031104 4
 
1.7%
0.00020424 3
 
1.3%
0.00017486 3
 
1.3%
0.00017987 3
 
1.3%
0.99683 3
 
1.3%
0.00011382 3
 
1.3%
Other values (165) 197
83.5%
ValueCountFrequency (%)
2.0889 × 10-51
 
0.4%
2.1081 × 10-51
 
0.4%
2.2331 × 10-51
 
0.4%
3.0307 × 10-51
 
0.4%
3.1176 × 10-51
 
0.4%
3.3214 × 10-54
1.7%
3.4833 × 10-51
 
0.4%
4.3568 × 10-52
0.8%
4.7263 × 10-51
 
0.4%
5.0256 × 10-51
 
0.4%
ValueCountFrequency (%)
0.99893 1
0.4%
0.99873 1
0.4%
0.99844 1
0.4%
0.99838 1
0.4%
0.99821 1
0.4%
0.99818 1
0.4%
0.99808 1
0.4%
0.99807 1
0.4%
0.99794 1
0.4%
0.9978 1
0.4%
Distinct87
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7889077
Minimum0.00106612
Maximum0.99998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-11-06T10:52:50.208116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.00106612
5-th percentile0.0028141125
Q10.99948
median0.999805
Q30.9999
95-th percentile0.9999525
Maximum0.99998
Range0.99891388
Interquartile range (IQR)0.00042

Descriptive statistics

Standard deviation0.40762891
Coefficient of variation (CV)0.51670039
Kurtosis0.014560951
Mean0.7889077
Median Absolute Deviation (MAD)0.000125
Skewness-1.4193182
Sum186.18222
Variance0.16616133
MonotonicityNot monotonic
2023-11-06T10:52:50.317344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99993 16
 
6.8%
0.99989 15
 
6.4%
0.99981 12
 
5.1%
0.9998 12
 
5.1%
0.99969 10
 
4.2%
0.99994 10
 
4.2%
0.99983 9
 
3.8%
0.99991 8
 
3.4%
0.99997 7
 
3.0%
0.99982 6
 
2.5%
Other values (77) 131
55.5%
ValueCountFrequency (%)
0.00106612 1
0.4%
0.00127462 1
0.4%
0.00155735 1
0.4%
0.00162275 1
0.4%
0.00179304 1
0.4%
0.00182139 1
0.4%
0.00191647 1
0.4%
0.00193323 1
0.4%
0.00205937 1
0.4%
0.00219584 1
0.4%
ValueCountFrequency (%)
0.99998 3
 
1.3%
0.99997 7
3.0%
0.99996 2
 
0.8%
0.99995 4
 
1.7%
0.99994 10
4.2%
0.99993 16
6.8%
0.99992 4
 
1.7%
0.99991 8
3.4%
0.9999 6
 
2.5%
0.99989 15
6.4%

Interactions

2023-11-06T10:52:44.473438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:27.546367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.741304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.879596image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.933055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.054782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.113604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.158109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.269444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.422609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.562351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.696596image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.924829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.000634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.107725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.194049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:44.546255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:27.631891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.820256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.943301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.996202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.124402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.178413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.225322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.333265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.495052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.630980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.766308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.987916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.066817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.185074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.267548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:44.618283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:27.702322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.885704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.992488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.042244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.191038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.240969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.297282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.399879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.566873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.702333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.838012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.068264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.137988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.254117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.340390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:44.687973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:27.751249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.950880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.066843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.117251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.242067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.302286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.364570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.460584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.632677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.767834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.905385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.132893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.201638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.320504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.408989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:44.741281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:27.819884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.014060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.128470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.160738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.307681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.359708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.427297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.509325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.697720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.832022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.968456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.191497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.264155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.381645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.473553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:44.826170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:27.964093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.077850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.191098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.236761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.371231image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.408660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.492883image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.582680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.766341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.899661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.024931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.253560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.330775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.444396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.542343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:44.897438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.014161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.143781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.253701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.296049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.425459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.482345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.559519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.645496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.833249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.967282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.106519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.308443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.398334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.508103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.612634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:44.971514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.090748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.212826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.323988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.360786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.501710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.548769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.629299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.709065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.906471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.042198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.176152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.386985image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.466732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.578690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.688645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:45.040024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.159388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.274519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.382514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.417797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.562706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.609706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.697973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.771850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.971935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.107286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.242464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.446512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.532803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.624887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.756437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:45.118081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.228654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.347599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.456422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.486585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.633079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.680306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.776393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.841560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.046504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.182732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.316033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.515227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.607230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.711116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.837324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:45.195472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.299228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.420146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.530143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.559103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.704260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.754301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.848085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.907501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.124987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.257416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.390995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.587045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.683735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.784334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.918896image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:45.274631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.368908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.493814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.600809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.626187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.773907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.823797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.920105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.074653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.198718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.331941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.467223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.658603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.758206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.855396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:44.090892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:45.340717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.433620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.564853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.662861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.692728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.826077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.875506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.985179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.145725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.266368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.398716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.533793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.708125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.825745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.918727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:44.168428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:45.407270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.500268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.658056image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.730491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.758927image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.907543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.955496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.057926image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.221636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.339240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.474522image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.607552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.801749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.898503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.990027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:44.244637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:45.485595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.580825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.735449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.794170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.832254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:32.971488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.019304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.125198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.287696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.408074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.543611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.677943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.863181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:41.965009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.041515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:44.316031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:45.562003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:28.657857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:29.809332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:30.863802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:31.899061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:33.043099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:34.089675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:35.196813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:36.355719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:37.482930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:38.621794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:39.755052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:40.925323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:42.041064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:43.124126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-06T10:52:44.391466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-06T10:52:50.511759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
CLIENTNUMCustomer_AgeDependent_countMonths_on_bookMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2Attrition_FlagGenderEducation_LevelMarital_StatusIncome_CategoryTotal_Relationship_Count
CLIENTNUM1.000-0.001-0.1440.090-0.0800.0460.003-0.1410.0160.0880.0620.1000.028-0.070-0.0230.0230.0000.1370.0000.0000.0720.105
Customer_Age-0.0011.000-0.2120.7730.0380.071-0.014-0.080-0.0130.066-0.017-0.099-0.070-0.0060.098-0.1000.0000.0000.0000.1440.0000.064
Dependent_count-0.144-0.2121.000-0.1530.0080.0240.076-0.0530.0810.0580.1460.0840.031-0.0980.106-0.1020.0390.0000.0650.0000.0400.000
Months_on_book0.0900.773-0.1531.0000.0740.074-0.044-0.116-0.0350.0730.066-0.035-0.098-0.0040.110-0.1100.0820.0000.0000.0950.0000.000
Months_Inactive_12_mon-0.0800.0380.0080.0741.0000.073-0.032-0.081-0.0230.027-0.036-0.079-0.049-0.0180.540-0.5420.2920.1070.0000.0000.0800.000
Contacts_Count_12_mon0.0460.0710.0240.0740.0731.000-0.034-0.142-0.0230.058-0.156-0.149-0.064-0.0180.647-0.6480.1750.0000.0800.0000.0000.119
Credit_Limit0.003-0.0140.076-0.044-0.032-0.0341.000-0.0080.9940.054-0.001-0.021-0.076-0.824-0.0630.0600.1570.2860.0230.0800.1820.000
Total_Revolving_Bal-0.141-0.080-0.053-0.116-0.081-0.142-0.0081.000-0.1010.0170.1380.1870.0950.503-0.2410.2420.5620.0000.0790.1500.0120.077
Avg_Open_To_Buy0.016-0.0130.081-0.035-0.023-0.0230.994-0.1011.0000.045-0.019-0.045-0.092-0.875-0.0340.0320.0000.2690.0000.1220.1500.040
Total_Amt_Chng_Q4_Q10.0880.0660.0580.0730.0270.0580.0540.0170.0451.0000.1070.0290.239-0.0190.087-0.0840.1720.0000.0000.0430.0410.009
Total_Trans_Amt0.062-0.0170.1460.066-0.036-0.156-0.0010.138-0.0190.1071.0000.8860.2950.108-0.2840.2860.4300.0000.0700.0000.0000.238
Total_Trans_Ct0.100-0.0990.084-0.035-0.079-0.149-0.0210.187-0.0450.0290.8861.0000.2780.159-0.3860.3860.5770.0000.0000.0170.0470.172
Total_Ct_Chng_Q4_Q10.028-0.0700.031-0.098-0.049-0.064-0.0760.095-0.0920.2390.2950.2781.0000.143-0.2080.2080.3730.0000.0000.0000.0350.000
Avg_Utilization_Ratio-0.070-0.006-0.098-0.004-0.018-0.018-0.8240.503-0.875-0.0190.1080.1590.1431.000-0.1120.1140.4690.1690.1270.1590.0700.136
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1-0.0230.0980.1060.1100.5400.647-0.063-0.241-0.0340.087-0.284-0.386-0.208-0.1121.000-0.9990.9870.0000.0000.1290.0740.186
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_20.023-0.100-0.102-0.110-0.542-0.6480.0600.2420.032-0.0840.2860.3860.2080.114-0.9991.0000.9870.0000.0000.1290.0740.186
Attrition_Flag0.0000.0000.0390.0820.2920.1750.1570.5620.0000.1720.4300.5770.3730.4690.9870.9871.0000.0000.0000.1290.0740.186
Gender0.1370.0000.0000.0000.1070.0000.2860.0000.2690.0000.0000.0000.0000.1690.0000.0000.0001.0000.0000.0000.8120.000
Education_Level0.0000.0000.0650.0000.0000.0800.0230.0790.0000.0000.0700.0000.0000.1270.0000.0000.0000.0001.0000.0000.0000.000
Marital_Status0.0000.1440.0000.0950.0000.0000.0800.1500.1220.0430.0000.0170.0000.1590.1290.1290.1290.0000.0001.0000.0000.048
Income_Category0.0720.0000.0400.0000.0800.0000.1820.0120.1500.0410.0000.0470.0350.0700.0740.0740.0740.8120.0000.0001.0000.000
Total_Relationship_Count0.1050.0640.0000.0000.0000.1190.0000.0770.0400.0090.2380.1720.0000.1360.1860.1860.1860.0000.0000.0480.0001.000

Missing values

2023-11-06T10:52:45.674734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-06T10:52:45.898125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2
22784725333Existing Customer41M3High SchoolMarried$40K - $60KBlue334214470.06803790.01.608931181.5710.1520.0000690.999930
38715190283Existing Customer57F1GraduateUnknown$40K - $60KBlue493323672.08862786.01.3201464280.5560.2410.0001690.999830
58711427458Existing Customer44F5GraduateMarriedUnknownBlue354126273.09785295.02.2751359251.0830.1560.0000570.999940
134711402333Existing Customer47M2DoctorateSingleLess than $40KBlue372133235.07972438.00.5411493321.0000.2460.0001340.999870
179710829108Existing Customer59F1UneducatedMarried$40K - $60KBlue364233356.09852371.00.350999280.5560.2940.0001850.999820
195720476733Existing Customer47F3High SchoolMarriedLess than $40KBlue342126028.07995229.00.8191002220.5710.1330.0000560.999940
258714187533Existing Customer45F2UneducatedSingle$40K - $60KBlue352303540.08492691.00.4561321280.7500.2400.0000680.999930
509716223708Attrited Customer45F3UneducatedSingleUnknownBlue363334028.07103318.00.731791220.8330.1760.9969100.003088
573715416633Existing Customer54M1UnknownMarried$80K - $120KBlue364126175.09605215.00.7091847590.4050.1550.0000570.999940
622711628458Existing Customer45F3GraduateMarriedLess than $40KBlue314108829.09017928.00.8251902450.6670.1020.0000210.999980
CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2
9875718299933Existing Customer42M3High SchoolMarried$40K - $60KBlue303344333.07393594.00.690159031000.7540.1710.0005210.999480
9895708880683Existing Customer49M5CollegeMarried$40K - $60KBlue404343970.08923078.00.772136971020.7590.2250.0005180.999480
9929779082633Existing Customer30M2UneducatedSingle$60K - $80KBlue133234107.09793128.00.647145961040.7330.2380.0001970.999800
9936709977933Attrited Customer52M5UneducatedMarried$80K - $120KBlue442349959.01999760.01.0038517600.5000.0200.9981800.001821
9993719934783Existing Customer43M3DoctorateMarried$80K - $120KBlue274223676.06733003.00.629146511050.7800.1830.0001710.999830
10037789398733Existing Customer56M3GraduateMarried$120K +Blue463325270.07794491.00.731161791050.6940.1480.0001870.999810
10041767348733Existing Customer56M2UnknownMarried$60K - $80KBlue493334058.07933265.00.758158651050.6670.1950.0003330.999670
10087713768358Existing Customer45M4GraduateSingle$40K - $60KBlue353227935.08887047.00.779153801220.6940.1120.0001190.999880
10099709094358Existing Customer51F1GraduateSingleLess than $40KBlue414238900.07988102.00.64716737880.6600.0900.0001800.999820
10121713899383Existing Customer56F1GraduateSingleLess than $40KBlue504143688.06063082.00.570145961200.7910.1640.0001480.999850